| Autor: |
Xingchen Lu, Dahai Jing, Defu Jiang, Ming Liu, Yiyue Gao, Chenyong Tian |
| Předmět: |
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| Zdroj: |
KSII Transactions on Internet & Information Systems; Jun2023, Vol. 17 Issue 6, p1635-1656, 22p |
| Abstrakt: |
In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
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